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{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"import numpy as np\n", | |
"import statsmodels.api as sm\n", | |
"import pymc3 as pm\n", | |
"#import theano.tensor as tt\n", | |
"from theano import tensor as T\n", | |
"from matplotlib import pylab as plt\n", | |
"import pystan\n", | |
"import seaborn as sns\n", | |
"\n", | |
"np.random.seed(1)\n", | |
"n1 = 3000\n", | |
"n2 = 1500\n", | |
"n = n1 + n2\n", | |
"\n", | |
"mu1 = 1\n", | |
"mu2 = 8\n", | |
"\n", | |
"size = 1.2\n", | |
"\n", | |
"data1 = np.random.negative_binomial(size, size/(mu1 + size), n1)\n", | |
"data2 = np.random.negative_binomial(size, size/(mu2 + size), n2)\n", | |
"data = np.concatenate([data1, data2])" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Applied interval-transform to p and added transformed p_interval_ to model.\n", | |
"Applied log-transform to size and added transformed size_log_ to model.\n", | |
" [-----------------100%-----------------] 20000 of 20000 complete in 65.8 sec" | |
] | |
} | |
], | |
"source": [ | |
"with pm.Model() as model:\n", | |
" p = pm.Uniform( \"p\", 0 , 1) \n", | |
" ber = pm.Bernoulli( \"ber\", p = p, shape=len(data)) \n", | |
"\n", | |
" size = pm.HalfCauchy('size', beta=2.5)\n", | |
"\n", | |
" mean = pm.Normal('mean', 0, sd=10, shape=2 )\n", | |
" mu = pm.Deterministic('mu', mean[ber])\n", | |
" process = pm.NegativeBinomial('obs', mu, alpha=size, observed=data)\n", | |
"\n", | |
"from scipy import optimize\n", | |
"with model:\n", | |
" start = pm.find_MAP(vars=[p, size, mean],fmin=optimize.fmin_powell)\n", | |
" #start['p'] = 0.5\n", | |
" #start['phi'] = 1\n", | |
" start[\"mean\"] = np.array([1, 8])\n", | |
" step1 = pm.Metropolis([p, size, mean])\n", | |
" step2 = pm.BinaryMetropolis([ber])\n", | |
" trace_ = pm.sample(20000, start=start, step=[step1, step2])" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"%matplotlib inline" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"trace = trace_[200::2]" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 6, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f51c2b01748>,\n", | |
" <matplotlib.axes._subplots.AxesSubplot object at 0x7f515397a940>]], dtype=object)" | |
] | |
}, | |
"execution_count": 6, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
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rmJTtMrZrY7kuJadEPBjD87za8bnaNxEzjIFBwS4SDUbwPA/X8/eOaj/X7+vVtjqug+05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\nEREREWm4/w9TV6hWHAP9bAAAAABJRU5ErkJggg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7f52004594a8>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"pm.traceplot(trace, ['mean'])" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 7, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
" [-----------------100%-----------------] 5000 of 5000 complete in 76.1 sec" | |
] | |
} | |
], | |
"source": [ | |
"with model:\n", | |
" start = pm.find_MAP(vars=[p, size, mean],fmin=optimize.fmin_powell)\n", | |
" start[\"mean\"] = np.array([1, 8])\n", | |
" step1 = pm.Slice([p, size, mean])\n", | |
" step2 = pm.BinaryMetropolis([ber])\n", | |
" trace_ = pm.sample(5000, start=start, step=[step1, step2])" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 8, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f51ab01a4e0>,\n", | |
" <matplotlib.axes._subplots.AxesSubplot object at 0x7f51aa643160>]], dtype=object)" | |
] | |
}, | |
"execution_count": 8, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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2EWnq7CmlHlMiIkeXMeZfA+8Cnu5y36PA/wd8H3AMeCfwn40xZw5zjFtZu+K/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\nRKQvFJgSEREREREREZG++P8OPZd1K2NWUwAAAABJRU5ErkJggg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7f51aba3beb8>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"trace = trace_[500::2]\n", | |
"pm.traceplot(trace, ['mean'])" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 9, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"\n", | |
"mean:\n", | |
"\n", | |
" Mean SD MC Error 95% HPD interval\n", | |
" -------------------------------------------------------------------\n", | |
" \n", | |
" 3.381 9.768 0.967 [-14.417, 22.466]\n", | |
" 3.306 0.077 0.002 [3.158, 3.456]\n", | |
"\n", | |
" Posterior quantiles:\n", | |
" 2.5 25 50 75 97.5\n", | |
" |--------------|==============|==============|--------------|\n", | |
" \n", | |
" -13.021 -3.927 3.528 10.151 24.960\n", | |
" 3.160 3.253 3.304 3.358 3.459\n", | |
"\n" | |
] | |
} | |
], | |
"source": [ | |
"pm.summary(trace, ['mean'])" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python [conda root]", | |
"language": "python", | |
"name": "conda-root-py" | |
}, | |
"language_info": { | |
"codemirror_mode": { | |
"name": "ipython", | |
"version": 3 | |
}, | |
"file_extension": ".py", | |
"mimetype": "text/x-python", | |
"name": "python", | |
"nbconvert_exporter": "python", | |
"pygments_lexer": "ipython3", | |
"version": "3.5.2" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 1 | |
} |
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